1. Field of the Invention
The present invention relates to a anomaly diagnosis system and a anomaly diagnosis method, for diagnosing a state of a machine facility.
2. Description of the Related Art
In various types of machine facilities such as construction machinery, medical devices, power generation facilities using wind power, photovoltaic power, or thermal power, water treatment plants, and other plants, a periodic maintenance is previously conducted to avoid an adverse influence on customers such as a decrease in operation ratio, un-achievement of the final specification due to deterioration in performance or quality, and deficiency in reliability. However, though the periodical maintenance is conducted, shut-down of a machine facility due to a trouble and a performance decreases cannot be avoided. Accordingly, a concept of monitoring a performance or a quality in addition to an earlier discovery of an error based on data of sensors added to the machinery facility becomes important in addition to an earlier discovery of an abnormality based on the data of the sensor added to the machine facility.
However, it was difficult to grasp a state of a machine facility from a large quantity of sensor data, machine facility information, and maintenance history information and predict remaining operable hours without any trouble (continuous operation hours of the facility) because it requires both knowledge of designing and the site and a large quantity of data analysis.
For example, JP2013-152655 A disclosed an abnormality diagnosis method for estimating an anomaly measure of the machine facility and an RUL (Remaining Useful Life) by applying Gaussian Process, a k-NN (k-nearest neighbor) method, and Particle filter method to multi-dimensional sensor data obtained from the machinery facility.
Further, JP2013-58099 A disclosed a technology in which a process state quantity of a plant is acquired, and a performance estimation index of the control system is calculated at every first period (for example one day) and calculates a trend using a performance estimation index value for a second period (for example, one month) which is longer than the first period (for example, one day).